Paper Info
Title
An improved PSO approach to short-term economic dispatch of cascaded hydropower plants
Abstract
Purpose - The purpose of this paper is to establish the optimization model and solve the short-term economic dispatch of cascaded hydro-plants. Design/methodology/approach - An improved particle swarm optimization (IPSO) approach is proposed to solve the short-term economic dispatch of cascaded hydroelectric plants. The water transport delay time between connected reservoirs is taken into account and it is easy in dealing with the difficult hydraulic and power coupling constraints using the proposed method in practical cascaded hydroelectric plants operation. The feasibility of the proposed method is demonstrated for actual cascaded hydroelectric plant. Findings - The simulation results show that this approach can prevent premature convergence to a high degree and keep a rapid convergence speed. Research limitations/implications - The optimal values of parameters in the proposed method are the main limitations where the method will be applied to the economic operation of the hydro-plant. Practical implications The paper presents useful advice for short-term economic operations of the hydro-plant. A new optimization method to solve the short-term optimal generation scheduling is proposed. The optimal generation power and water discharge during the whole dispatching time for hydro-plant operation can be obtained. Originality/value - The IPSO method is realized by maintaining high diversity of the swarm during the optimization process and preventing premature convergence.
Year
DOI
Venue
2010
10.1108/03684921011063664
KYBERNETES
Keywords
Field
DocType
Cybernetics,Optimization techniques,Electric power stations,Constraint handling
Particle swarm optimization,Transport delay,Hydropower,Economic dispatch,Mathematical optimization,Premature convergence,Computer science,Rapid convergence,Hydroelectricity,Cybernetics
Journal
Volume
Issue
ISSN
39
8
0368-492X
Citations 
PageRank 
References 
2
0.48
2
Authors
2
Name
Order
Citations
PageRank
Yanbin Yuan114017.67
Xiaohui Yuan2364.61